Jneopallium – Biologically-grounded Java framework for natural neuron networks (safety-first autonomous AI)

Hi everyone,I’m Dmytro Rakovskyi, independent researcher. For the last few months I’ve been developing Jneopallium — an open-source Java framework for modeling biologically realistic neuron networks at customizable levels of detail.Core features: * Typed signals with explicit fast/slow loop frequencies (bioelectric + neuromodulatory timescales) * Multi-receptor neurons (each neuron can implement multiple interfaces with dedicated processors) * Full autonomous-AI architecture with 28 neuron classes, including: * Harm discriminator (consequence-model safety gate with asymmetric caution learning and hard ethical invariants) * Loop-prevention subsystem (detects and gently breaks runaway cycles without permanent damage) * Embodiment, affect, curiosity, glia, sleep, and working memory modules * Optional non-blocking LLM integration with strict verification * Designed for real-world deployment (JVM + planned FPGA/gRPC backend) The goal is to build safe, interpretable, biologically-plausible autonomous systems for robotics, BCI, industrial control, and clinical decision support.I’m looking for: * People interested in using Jneopallium in their projects * Collaborators (code, testing, new modules, hardware integration) * Feedback from the SNN / neuromorphic / AI-safety / embodied-AI communities Repo: https://github.com/rakovpublic/jneopallium would love to hear your thoughts or discuss possible collaboration. Even simple feedback on architecture or use-cases is very welcome.Thank you! Dmytro Rakovskyi